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Home Page: https://doi.org/10.21105/joss.05925
License: BSD 3-Clause "New" or "Revised" License
Chi is an open source Python package which is designed for treatment response modelling.
Home Page: https://doi.org/10.21105/joss.05925
License: BSD 3-Clause "New" or "Revised" License
In this issue I am creating a notebook that takes the lung cancer low dose data from [1], imports it, cleans it, and exports the cleaned data file
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)
Similar to PDTimeSeriesPlot
Create a figure that
['ID', 'Parameter', 'Estimate' , 'Score', 'Run']
See #6.
This figure is not supposed to assess convergence, but compare marginal posteriors across individuals
Deploy pd simulator with Heroku
A PopulationModel that leaves all parameters independent.
Create a notebook that solves the tumour growth PD model with the error model suggested by Eigenmann et al.
By default
See #19 for details.
Building on the PosteriorConvergencePlot build an app that
The class should be initialised with a
Has method
Probably just because y axis update has to be done for yaxis1, yaxis2 etc.
Create a problem class that
brew update-reset sometimes takes forever to setup Homebrew for sundials. Check whether there is a more light weight command.
Create a SamplingControler similar to the OptimisationController and building on pints.MCMCController
See #26 for infos on MarginalPosteriorPlot
The controller takes a
When this is done, complete #56 by 'hacking', i.e. set ID in dataset to pooled for all individuals and run optimisation.
In this issue I am creating a notebook that takes the lung cancer medium dose data from [1], imports it, cleans it, and exports the cleaned data file
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)
The PDDataPlot has been generalised to a PDTimeSeriesPlot.
In this issue I am creating a notebook that takes the lung cancer control growth data from [1], imports it, cleans it, and exports the cleaned data file
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)
Setup docs generation with Sphinx and test documentation
Create a notebook in that
Add an exemplary PK dataset to the data library
Set mean dose manually for now and ignore dose in dataset
In this issue I am creating a notebook that takes the lung cancer control growth data from [1], imports it, cleans it, and exports the cleaned data file
[1] Eigenmann et. al., Combining Nonclinical Experiments with Translational PKPD Modeling to Differentiate Erlotinib and Gefitinib, Mol Cancer Ther (2016)
Infer PK model parameters indiviudally.
If necessary fix model parameters similar to the reference
For informative priors it is worth checking how much information was gained from the data.
Similar to Pharmacodynamic model, but
This notebook should
Use set_population_model method
Along the lines of #24 but now pool all parameters across individuals.
Create a log-likelihood that
Create a log-posterior class similar to pints.LogPosterior
Additional features:
A HierarchicalLogLikelihood
Implement support in the ProblemModellingController
Create a figure that takes samples from one individual and plots marginal posterior with trace.
This class is mainly to assess convergence of the traces.
Similar to individual inference, but now pool noise.
Both optimisation and sampling will likely fail with the prior settings
I forgot to transform the parameters.
Following the ProblemModellingController class in #43, refactor the OptimisationController such that
Similar to PDSimulationController
Extends #24.
The inference has been performed rather rash and not very controlled. Add the following features:
Create a SimulationController dash app with the following features
Possible extensions:
Refactor the sampling controller similar to #44 .
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